Project Steps :

The steps of the project are the following and below are the pipeline stages :

  1. Compute the camera calibration matrix and distortion coefficients based on provided chessboard images.
  2. Undistort the Image.
  3. Create Thresholded binary image by applying color transform and gradient
  4. Apply perspective transform to get birds-eye view
  5. Detect Lane pixels in the warped viewd
  6. Calculate the curvature of lanes and offset from center of lane
  7. Unwarp the lane markings back to original domain
  8. Superimpose the lane markings int he original image and apply these on a video frame by frame

Steps : Compute the camera calibration matrix and distortion coefficients given a set of chessboard images and undistort the image.

a. Open CV function calibrateCamera is used to find the camera matrix to calibrate the camera

b. The chess board images in camera_cal folder is used for calibration (9x6 points)

In [27]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
import glob
import pickle

# prepare object points
nx = 9#TODO: enter the number of inside corners in x
ny = 6#TODO: enter the number of inside corners in y

# Initialise image and object point arrays
objpoints = []  # this is the 3d domain pts
imgpoints = []  # this the distorted points in 2d domain

# Generate object points
objp = np.zeros((nx*ny,3), np.float32)
objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2) # x, y coordinates

def cal_undistort(img, objpoints, imgpoints):
    # Calibrate camera
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img.shape[0:2], None, None)
    # Undistort image
    undist = cv2.undistort(img, mtx, dist, None, mtx)
    return undist


#Read Camera calibration images
images = glob.glob("camera_cal/calibration*.jpg")

for fname in images :
 img = cv2.imread(fname)
 #plt.imshow(img)
 # Convert to grayscale
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

 # Find the chessboard corners
 ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
 #print(ret)
 # If found, draw corners
 if ret == True:
    imgpoints.append(corners)
    objpoints.append(objp)
    # Draw and display the corners
    cv2.drawChessboardCorners(img, (nx, ny), corners, ret)
    plt.imshow(img)
In [5]:
import pickle
#calibrate camera and save camera matrix as pickle file
img_size = (img.shape[1], img.shape[0])
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
cam_pickle = {}
cam_pickle["mtx"] = mtx
cam_pickle["dist"] = dist
pickle.dump( cam_pickle, open( "camera_matrix.p", "wb" ) )

Here are the original image and undistored images of chess board after applying camera calibraion.

In [26]:
# Test undistortion on an image
img = cv2.imread('camera_cal/calibration1.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points

out = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('output_images/orig_calibration1.jpg',img)
cv2.imwrite('output_images/undist_calibration1.jpg',out)

f, (im1, im2) = plt.subplots(1, 2, figsize=(20,10))
im1.imshow(img)
im1.set_title('Original Image', fontsize=20)
im2.imshow(out)
im2.set_title('Undistorted Image', fontsize=20)
Out[26]:
<matplotlib.text.Text at 0x7fae2fd7a0f0>
In [ ]:
 

Start of Image Pipeline :

  1. Read Image
  2. Undistort the image using the camera matrix calculated above
  3. Apply gradient and color transformation to capture the Lane markings
  4. Apply Perspective Traform

Read Image and apply distortion correction Below are sample images from test_images before and after undistortion.

In [17]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
%matplotlib inline


with open("camera_matrix.p", mode='rb') as f:
    cam_pickle = pickle.load(f)
mtx = cam_pickle["mtx"]
dist = cam_pickle["dist"]


read_img = cv2.imread("test_images/test2.jpg")
#read_image = mpimg.imread("test_images/test1.jpg")
#since opencv reads image in bgr format
image_corr=cv2.cvtColor(read_img,cv2.COLOR_BGR2RGB)

undistorted_input = cv2.undistort(image_corr, mtx, dist, None, mtx)
cv2.imwrite('output_images/undist_test1.jpg',undistorted_input)
imshape = image_corr.shape
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(50,50))
ax1.imshow(image_corr)
ax1.set_title('Original Image', fontsize=20)
ax2.imshow(undistorted_input)
ax2.set_title('Undistorted Image', fontsize=20)
Out[17]:
<matplotlib.text.Text at 0x7fae340690f0>

Apply Gradients, thresholding and Color Transforms (S-Domain) for Binary Image. See transform_pipeline function to see how these are applied.

In [25]:
from primitives import *
sx_thresh = (30,100)
s_thresh =(160,255)

combined_binary = transform_pipeline(undistorted_input, s_thresh, sx_thresh )
plt.imshow(combined_binary, cmap="gray")
cv2.imwrite('output_images/grad_test1.jpg',combined_binary)
Out[25]:
True

Below is the image with only the region of interest

In [9]:
vertices = np.array([[(0,imshape[0]),(650, 430), (730, 430), (imshape[1],imshape[0])]], dtype=np.int32)
masked_image = region_of_interest(combined_binary, vertices)
plt.imshow(masked_image, cmap="gray")
Out[9]:
<matplotlib.image.AxesImage at 0x7fae3425ab38>

Calculate the perspective tranform factors. The src and dst vectors were obtained by manually reviewing the test images provided. Below is the image with perspective transform applied on the above binary image.

In [18]:
src = np.float32(
    [[300, 680],
     [600, 460],
     [730, 460],
     [1080, 680]])

dst = np.float32(
    [[300,680],
     [300,0],
     [1000,0],
     [1080,680]])

M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
warped_img = cv2.warpPerspective(combined_binary, M, (imshape[1], imshape[0]), flags=cv2.INTER_LINEAR)
plt.imshow(warped_img, cmap="gray")
cv2.imwrite('output_images/warped_img_test1.jpg',warped_img)


import numpy as np
#histogram = np.sum(warped_img[warped_img.shape[0]//2:,:], axis=0)
#plt.plot(histogram)

Fit curves for lane markings based on the warped binary image from above. This is the same pipeline used in class.

In [28]:
 
import numpy as np
import cv2
import matplotlib.pyplot as plt

# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
def find_lane(binary_warped, draw=True):
    
 histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
 # Create an output image to draw on and  visualize the result
 out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
 # Find the peak of the left and right halves of the histogram
 # These will be the starting point for the left and right lines
 midpoint = np.int(histogram.shape[0]/2)
 leftx_base = np.argmax(histogram[:midpoint])
 rightx_base = np.argmax(histogram[midpoint:]) + midpoint

 # Choose the number of sliding windows
 nwindows = 9
 # Set height of windows
 window_height = np.int(binary_warped.shape[0]/nwindows)
 # Identify the x and y positions of all nonzero pixels in the image
 nonzero = binary_warped.nonzero()
 nonzeroy = np.array(nonzero[0])
 nonzerox = np.array(nonzero[1])
 # Current positions to be updated for each window
 leftx_current = leftx_base
 rightx_current = rightx_base
 # Set the width of the windows +/- margin
 margin = 100
 # Set minimum number of pixels found to recenter window
 minpix = 50
 # Create empty lists to receive left and right lane pixel indices
 left_lane_inds = []
 right_lane_inds = []

# Step through the windows one by one
 for window in range(nwindows):
    # Identify window boundaries in x and y (and right and left)
    win_y_low = binary_warped.shape[0] - (window+1)*window_height
    win_y_high = binary_warped.shape[0] - window*window_height
    win_xleft_low = leftx_current - margin
    win_xleft_high = leftx_current + margin
    win_xright_low = rightx_current - margin
    win_xright_high = rightx_current + margin
    # Draw the windows on the visualization image
    cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) 
    cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
    # Identify the nonzero pixels in x and y within the window
    good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
    good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
    # Append these indices to the lists
    left_lane_inds.append(good_left_inds)
    right_lane_inds.append(good_right_inds)
    # If you found > minpix pixels, recenter next window on their mean position
    if len(good_left_inds) > minpix:
        leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
    if len(good_right_inds) > minpix:        
        rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

 # Concatenate the arrays of indices
 left_lane_inds = np.concatenate(left_lane_inds)
 right_lane_inds = np.concatenate(right_lane_inds)

 # Extract left and right line pixel positions
 leftx = nonzerox[left_lane_inds]
 lefty = nonzeroy[left_lane_inds] 
 rightx = nonzerox[right_lane_inds]
 righty = nonzeroy[right_lane_inds] 

 # Fit a second order polynomial to each
 left_fit = np.polyfit(lefty, leftx, 2)
 right_fit = np.polyfit(righty, rightx, 2)
 
 
 ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
 left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
 right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]

 out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
 out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
 if(draw) :   
  plt.imshow(out_img)
  plt.plot(left_fitx, ploty, color='yellow')
  plt.plot(right_fitx, ploty, color='yellow')
  plt.xlim(0, 1280)
  plt.ylim(720, 0)
  cv2.imwrite('output_images/lane_markings_test1.jpg',out_img)

 return leftx, lefty, rightx, righty, left_fit , right_fit, out_img , ploty, left_fitx, right_fitx


binary_warped = np.zeros_like(warped_img[:,:])
binary_warped[(warped_img[:,:] > 0)] = 1

leftx, lefty, rightx, righty, left_fit , right_fit , lane_img , ploty, left_fitx, right_fitx = find_lane(binary_warped)

print(righty)
print(left_fit)
print(right_fit)
[640 640 640 ...,  79  79  79]
[ -2.62065907e-04   4.53339222e-01   1.47983515e+02]
[ -3.55459869e-04   6.77930305e-01   7.92840833e+02]

Define Functions for calculating curvature and offset. The radius of curvature is transformed from the pixel based y_to_fit and x_to_fit params to meters and a new polynomal is fit for this. The road curvature is assumed to be circular.

For the position of the vehicle, the camera is assumed to be mounted at the center of the car and the deviation of the midpoint of the lane from the center of the image is the output offset. It is assumed that lane_width is 3.7m and pixel wise x and y values are converted to meter scale.

Below is the image with Unwarped lane markings in the orignal image along with curvature and lane offset statistics embedded.

In [13]:
def calc_curvature(y_to_fit, x_to_fit, y_eval):
    # Conversion factors for pixels to meters
    m_per_pix_y, m_per_pix_x = 30/720, 3.7/700 # taken from class
    
    # Fit a new polynomial to world-space (in meters)
    fit = np.polyfit(y_to_fit*m_per_pix_y, x_to_fit*m_per_pix_x, 2)
    curvature = ((1 + (2*fit[0]*(y_eval*m_per_pix_y) + fit[1])**2)**1.5) / np.absolute(2*fit[0])
    return curvature


def calc_offset(left_x, right_x, img_center_x):
    lane_width = abs(left_x - right_x)
    lane_center_x = (left_x + right_x)//2
    pix_offset = img_center_x - lane_center_x
    lane_width_m = 3.7  
    return lane_width_m * (pix_offset/lane_width)
        
left_curv =  calc_curvature(lefty, leftx, np.max(ploty))
right_curv  =  calc_curvature(righty, rightx, np.max(ploty))  
print(left_curv)
print(right_curv)
offset = calc_offset(left_fitx[-1], right_fitx[-1], binary_warped.shape[1]//2)
print(offset)
626.750431532
462.32225106
-0.375829124117
In [20]:
#Overlap orig image with lane polygon
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))

# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
newwarp = cv2.warpPerspective(color_warp, Minv, (undistorted_input.shape[1], undistorted_input.shape[0])) 

# Combine the result with the original image
result = cv2.addWeighted(undistorted_input, 1, newwarp, 0.3, 0)
cv2.imwrite('output_images/final_lane_polygon_test1.jpg',result)

cv2.putText(result,'Radius of Left Curvature: %.2fm' % left_curv,(20,40), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2)
cv2.putText(result,'Radius of Right Curvature: %.2fm' % right_curv,(20,80), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2)
cv2.putText(result,'Center Lane Offset: %.2fm' % offset,(20,120), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255), 2)
plt.imshow(result)
Out[20]:
<matplotlib.image.AxesImage at 0x7fae340bf518>

Consolidate the entire image pipeline described above to a single function to be used for video processing. Additional metrics added : We save the previous frame's lane markings and maintain the lane markings from previous frame if the radius of curvature is predicted to be below a threshold of 460 m and the offset is less than -1.5 or greater than 1.5 m. This is to prevent cases where the pipeline predicts certain lane markings to be wrong due to shadows or changes in road texture, and this transform seems to fit well for the test video.

In [21]:
## Full Image pipeline

import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
%matplotlib inline


with open("camera_matrix.p", mode='rb') as f:
    cam_pickle = pickle.load(f)
mtx = cam_pickle["mtx"]
dist = cam_pickle["dist"]


def keep_last_fit(left_curve, right_curve,offset):
    if right_curve < 460 or left_curve <  460 or offset > 1.5 or offset < -1.5:
        return False
    else:
        return True


last_left_fit = None
last_right_fit = None
last_left_fitx = None
last_right_fitx = None
last_ploty = None

def image_pipeline(input_file, filepath=False):
 global last_left_fit
 global last_right_fit
 global last_left_fitx
 global last_right_fitx
 global last_ploty
    
 plt.clf()
   
 if filepath == True:
        # Read in image
        read_img = cv2.imread(input_file)
 else:
        read_img = input_file

 #image_corr=cv2.cvtColor(read_img,cv2.COLOR_BGR2BGR)
 
 undistorted_input = cv2.undistort(read_img, mtx, dist, None, mtx)
 imshape = image_corr.shape

#### Edge Detection
 sx_thresh = (30,100)
 s_thresh =(160,255)
 combined_binary = transform_pipeline(undistorted_input, s_thresh, sx_thresh )
######## Apply perspective transform
 src = np.float32(
    [[300, 680],
     [600, 460],
     [730, 460],
     [1080, 680]])

 dst = np.float32(
    [[300,680],
     [300,0],
     [1000,0],
     [1080,680]])

 M = cv2.getPerspectiveTransform(src, dst)
 Minv = cv2.getPerspectiveTransform(dst, src)
 warped_img = cv2.warpPerspective(combined_binary, M, (imshape[1], imshape[0]), flags=cv2.INTER_LINEAR)
####### find lane     
 binary_warped = np.zeros_like(warped_img[:,:])
 binary_warped[(warped_img[:,:] > 0)] = 1

 leftx, lefty, rightx, righty, left_fit , right_fit , lane_img , ploty, left_fitx, right_fitx = find_lane(binary_warped,False)

###### calc curvatur and curv
 left_curv =  calc_curvature(lefty, leftx, np.max(ploty))
 right_curv  =  calc_curvature(righty, rightx, np.max(ploty))  
 print(left_curv)
 print(right_curv)
 offset = calc_offset(left_fitx[-1], right_fitx[-1], binary_warped.shape[1]//2)
 print(offset)
 
 if(keep_last_fit(left_curv,right_curv,offset)) :
    last_left_fitx = left_fitx
    last_right_fitx = right_fitx
    last_ploty = ploty 
 else :
    left_fitx = last_left_fitx
    right_fitx = last_right_fitx
    ploty = last_ploty  
#Overlap orig image with lane polygon
 warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
 color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

 pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
 pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
 pts = np.hstack((pts_left, pts_right))

# Draw the lane onto the warped blank image
 cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
 newwarp = cv2.warpPerspective(color_warp, Minv, (undistorted_input.shape[1], undistorted_input.shape[0])) 

# Combine the result with the original image
 result = cv2.addWeighted(undistorted_input, 1, newwarp, 0.3, 0)

 cv2.putText(result,'Radius of Left Curvature: %.2fm' % left_curv,(20,40), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2)
 cv2.putText(result,'Radius of Right Curvature: %.2fm' % right_curv,(20,80), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255),2)
 cv2.putText(result,'Center Lane Offset: %.2fm' % offset,(20,120), cv2.FONT_HERSHEY_SIMPLEX, 1,(255,255,255), 2)
 plt.imshow(result)
 cv2.imwrite('output_images/final_lane_markings_all_pipe_test1.jpg',result)   
    
 return result

combined_img = image_pipeline(read_img)

  
626.750431532
462.32225106
-0.375829124117

Test the pipeline on video

In [141]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
output = 'project_output_colour.mp4'
clip1 = VideoFileClip("project_video.mp4")
output_clip = clip1.fl_image(image_pipeline) #NOTE: this function expects color images!!
%time output_clip.write_videofile(output, audio=False)
631.838900051
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-0.429192560058
[MoviePy] >>>> Building video project_output_colour.mp4
[MoviePy] Writing video project_output_colour.mp4
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588.793315863
-0.367972766961
  3%|▎         | 32/1261 [00:04<02:54,  7.05it/s]
673.426599192
734.011650133
-0.375264501105
619.860896962
654.189655343
-0.371559124405
  3%|▎         | 34/1261 [00:05<02:50,  7.20it/s]
629.443920025
1202.65311782
-0.41065764304
572.630291613
1639.41458315
-0.4107723719
  3%|▎         | 35/1261 [00:05<02:48,  7.27it/s]
575.517239275
1218.04798552
-0.408716991841
552.91430013
751.017704291
-0.385614773625
  3%|▎         | 38/1261 [00:05<03:07,  6.52it/s]
528.630150675
695.02272364
-0.378461641295
498.363116432
607.894839221
-0.371958737998
  3%|▎         | 40/1261 [00:06<02:55,  6.95it/s]
472.363202143
584.370207351
-0.372453730236
465.484685804
426.238658504
-0.356909900923
  3%|▎         | 41/1261 [00:06<02:53,  7.04it/s]
446.060835726
490.986914265
-0.368423994644
443.204881904
653.937573853
-0.384355690729
  3%|▎         | 44/1261 [00:06<03:03,  6.64it/s]
460.253152712
677.047892524
-0.398913897466
470.546248991
663.647380791
-0.403441155141
  4%|▎         | 46/1261 [00:07<03:01,  6.70it/s]
488.414059584
735.250532832
-0.430058147926
489.59372426
1009.96620814
-0.43316215306
  4%|▎         | 47/1261 [00:07<03:17,  6.13it/s]
528.619977835
874.847157061
-0.423497383635
519.756127945
1033.29686492
-0.435969600731
  4%|▍         | 50/1261 [00:07<03:09,  6.38it/s]
563.135798165
996.120587579
-0.437454906273
588.428768171
669.138315988
-0.420311995438
  4%|▍         | 51/1261 [00:07<03:08,  6.43it/s]
634.370474353
681.991963776
-0.426437554801
670.519037527
902.731595166
-0.438088020659
  4%|▍         | 54/1261 [00:08<03:05,  6.49it/s]
691.563715733
537.948687796
-0.416096519451
718.952269952
504.134980069
-0.411243384888
  4%|▍         | 55/1261 [00:08<03:08,  6.41it/s]
784.999311826
609.917521784
-0.423900967736
  4%|▍         | 56/1261 [00:08<03:24,  5.90it/s]
863.471748936
769.848024908
-0.427111308702
1012.48318879
635.039285385
-0.432767293696
  5%|▍         | 59/1261 [00:09<03:00,  6.68it/s]
992.155169739
1187.30808123
-0.458768871822
1000.37006701
1057.03717458
-0.43706306548
  5%|▍         | 60/1261 [00:09<02:56,  6.79it/s]
1027.1469075
777.471790697
-0.421832669223
852.485442715
1030.64157054
-0.414095063489
  5%|▍         | 63/1261 [00:09<03:21,  5.95it/s]
804.089797694
787.330948534
-0.385797025308
774.390406757
2316.12044257
-0.408356271683
  5%|▌         | 65/1261 [00:10<03:05,  6.45it/s]
748.636752243
559.965086626
-0.359063371034
696.488406203
633.884141943
-0.3470734068
  5%|▌         | 67/1261 [00:10<02:52,  6.90it/s]
666.14982922
617.246415127
-0.337079221174
650.728576849
1001.5933065
-0.344001820357
  5%|▌         | 69/1261 [00:10<02:52,  6.93it/s]
689.001862012
902.029805672
-0.339973199239
667.175560086
1009.43782329
-0.335705701318
  6%|▌         | 71/1261 [00:10<02:47,  7.12it/s]
658.870690006
2422.17598289
-0.356170929077
673.090538085
1246.92077578
-0.322707104043
  6%|▌         | 73/1261 [00:11<02:50,  6.96it/s]
743.575070423
743.759076475
-0.299713841271
796.266393562
627.677098676
-0.292457400587
  6%|▌         | 75/1261 [00:11<02:45,  7.14it/s]
859.037644353
862.156603388
-0.312981556478
994.610549743
891.401642837
-0.308416673001
  6%|▌         | 76/1261 [00:11<02:48,  7.02it/s]
1064.56347997
610.624457399
-0.294028167627
1280.6601186
993.622442143
-0.316715477198
  6%|▌         | 78/1261 [00:12<02:51,  6.89it/s]
1538.75687308
726.436000175
-0.303796274875
1674.24947492
493.495415308
-0.285625644795
  6%|▋         | 81/1261 [00:12<03:07,  6.30it/s]
1605.58324975
541.347712827
-0.283720765704
1805.83371006
666.38815334
-0.287949386962
  7%|▋         | 82/1261 [00:12<03:14,  6.08it/s]
1952.22361609
636.258171132
-0.282273608399
1709.90591321
776.72596586
-0.283319263342
  7%|▋         | 84/1261 [00:13<03:08,  6.24it/s]
1796.51560673
1079.97133284
-0.296428929629
1567.83174209
1235.12271771
-0.290491472691
  7%|▋         | 87/1261 [00:13<03:00,  6.52it/s]
1332.25340117
1434.49763889
-0.284542475213
1069.07117687
859.280405639
-0.260712440369
  7%|▋         | 88/1261 [00:13<02:56,  6.65it/s]
925.116922775
1351.69760348
-0.263306330734
782.719006329
1861.61956127
-0.266747155416
  7%|▋         | 91/1261 [00:14<02:46,  7.03it/s]
676.575316055
573.11453137
-0.227117571975
664.006376692
600.802731898
-0.230764302493
  7%|▋         | 92/1261 [00:14<02:50,  6.84it/s]
661.124244191
733.733153637
-0.239289224803
641.651563598
767.011948731
-0.244707817169
  8%|▊         | 95/1261 [00:14<02:57,  6.58it/s]
636.764204743
832.664193776
-0.249937150932
640.301750802
895.114115069
-0.245811485247
  8%|▊         | 96/1261 [00:14<02:53,  6.72it/s]
645.164722672
757.575331224
-0.226024386047
647.142186652
1206.56380198
-0.259651681129
  8%|▊         | 98/1261 [00:15<02:49,  6.85it/s]
672.955275336
736.917623399
-0.235559335364
681.243590417
791.254265908
-0.249306299661
  8%|▊         | 100/1261 [00:15<02:59,  6.45it/s]
696.756678473
780.826931531
-0.249901235304
704.93037461
688.173311934
-0.246935456191
  8%|▊         | 103/1261 [00:15<02:50,  6.78it/s]
699.659030466
653.173836472
-0.251870553822
689.239930976
502.906309124
-0.243485473565
  8%|▊         | 105/1261 [00:16<02:51,  6.75it/s]
700.103688786
527.89917033
-0.256974641262
675.752753519
605.670047969
-0.263813305091
  8%|▊         | 107/1261 [00:16<02:43,  7.05it/s]
734.74807688
666.944333879
-0.282531609178
739.198889332
759.499690591
-0.290240503481
  9%|▊         | 108/1261 [00:16<02:46,  6.92it/s]
683.638726302
817.192067376
-0.310833145847
683.877856175
1149.19896069
-0.326514087983
  9%|▊         | 110/1261 [00:16<02:46,  6.91it/s]
710.314436638
1366.04568813
-0.335525482363
656.789483108
1237.39484964
-0.331538004946
  9%|▉         | 113/1261 [00:17<02:48,  6.81it/s]
677.318393802
1083.38551721
-0.327341132053
667.461603433
723.822180407
-0.313024480371
  9%|▉         | 114/1261 [00:17<02:56,  6.49it/s]
633.544114453
697.899301332
-0.313199156933
582.706252832
821.24239191
-0.325556439025
  9%|▉         | 116/1261 [00:17<02:57,  6.45it/s]
560.436824205
442.978696607
-0.297572801722
563.759724423
485.093751482
-0.310033225192
  9%|▉         | 118/1261 [00:18<02:56,  6.49it/s]
561.875551729
555.022901198
-0.323639731195
567.864860527
520.227856207
-0.333379058536
 10%|▉         | 120/1261 [00:18<02:53,  6.59it/s]
556.536369978
555.524987657
-0.342891026084
554.722625973
639.120955508
-0.359172669872
 10%|▉         | 122/1261 [00:18<02:51,  6.64it/s]
548.218095058
657.118562559
-0.360190563537
534.506216303
497.906204391
-0.34680870503
 10%|▉         | 124/1261 [00:19<02:59,  6.34it/s]
531.638600664
577.770332682
-0.367276313858
530.782974128
524.942086802
-0.360585891436
 10%|▉         | 126/1261 [00:19<03:22,  5.60it/s]
564.760844339
624.6932549
-0.381727587325
545.138937213
482.436885679
-0.374116058757
 10%|█         | 128/1261 [00:19<03:14,  5.83it/s]
569.354640498
455.498208339
-0.374960319802
527.71186215
489.312392233
-0.383242944295
 10%|█         | 130/1261 [00:20<03:06,  6.06it/s]
559.436581025
607.179398971
-0.390942961339
607.50170035
577.100809173
-0.406884234281
 10%|█         | 132/1261 [00:20<03:29,  5.38it/s]
592.502617837
589.572543944
-0.411971787882
627.288531953
545.57364534
-0.398424072398
 11%|█         | 134/1261 [00:20<03:27,  5.43it/s]
622.438367333
822.57842399
-0.441316493225
658.579236903
534.320964514
-0.417258373969
 11%|█         | 137/1261 [00:21<03:11,  5.86it/s]
668.86706774
653.366823578
-0.435967828759
729.959804488
815.47109469
-0.446303390676
 11%|█         | 138/1261 [00:21<03:23,  5.51it/s]
745.712724323
883.837046512
-0.444513281357
728.936018171
913.113702538
-0.438645432751
 11%|█         | 140/1261 [00:21<03:24,  5.48it/s]
746.455071503
1737.20031297
-0.450040221737
737.340990503
763.499295527
-0.428150332631
 11%|█▏        | 142/1261 [00:22<03:11,  5.84it/s]
748.033614013
598.663357483
-0.416014673933
783.903817342
831.104122487
-0.429308802427
 11%|█▏        | 144/1261 [00:22<03:07,  5.95it/s]
778.659511386
822.995140585
-0.420472464477
796.298309937
880.047698321
-0.424105959432
 12%|█▏        | 146/1261 [00:22<02:59,  6.21it/s]
801.989038208
1292.51982296
-0.435008771875
820.664297245
1295.67046863
-0.427167827395
 12%|█▏        | 148/1261 [00:23<02:52,  6.44it/s]
765.133506861
674.524033361
-0.407693619083
811.132838824
666.93347751
-0.408975618923
 12%|█▏        | 150/1261 [00:23<02:53,  6.39it/s]
829.384738863
755.229819338
-0.411449647528
853.740770035
668.713958261
-0.403927155818
 12%|█▏        | 153/1261 [00:23<02:48,  6.59it/s]
900.403707591
625.055856628
-0.398743575374
801.787232777
460.002183723
-0.377236132236
 12%|█▏        | 154/1261 [00:24<02:54,  6.34it/s]
861.893790705
496.308017413
-0.388306100817
880.332077368
590.280038543
-0.399310684698
 12%|█▏        | 156/1261 [00:24<03:33,  5.18it/s]
892.931856903
679.099378601
-0.398172487244
 12%|█▏        | 157/1261 [00:24<03:32,  5.19it/s]
870.33779356
684.202164708
-0.392601338586
755.975412859
2396.92176046
-0.443231624828
 13%|█▎        | 159/1261 [00:25<03:23,  5.41it/s]
758.218521643
1166.74714889
-0.402368004627
694.081141392
1103.59505748
-0.396833499429
 13%|█▎        | 161/1261 [00:25<03:26,  5.33it/s]
725.350823961
1088.12166782
-0.388386926124
696.58059399
1102.59229948
-0.379175964064
 13%|█▎        | 163/1261 [00:25<03:18,  5.53it/s]
713.838054629
1431.74884679
-0.382022942362
655.207417657
2523.15715324
-0.388071842991
 13%|█▎        | 165/1261 [00:26<03:25,  5.33it/s]
631.566760141
650.084450035
-0.347498279206
642.709409057
631.185201653
-0.348007952898
 13%|█▎        | 167/1261 [00:26<03:13,  5.65it/s]
621.85451424
899.650355075
-0.355026633825
721.824225212
1024.41760993
-0.365062277682
 13%|█▎        | 169/1261 [00:26<03:14,  5.61it/s]
754.323492369
903.169543095
-0.360761324913
764.824902629
855.511735192
-0.35139427798
 14%|█▎        | 171/1261 [00:27<03:08,  5.80it/s]
843.340726224
692.578577242
-0.32490346408
873.529131785
657.514555935
-0.316670690848
 14%|█▎        | 173/1261 [00:27<02:59,  6.06it/s]
875.077383884
597.797278
-0.312384652399
811.216379776
427.270288033
-0.27921504318
 14%|█▍        | 175/1261 [00:27<03:02,  5.95it/s]
782.77337448
348.928241506
-0.257087425463
783.840837823
426.400315432
-0.280991098777
 14%|█▍        | 178/1261 [00:28<02:54,  6.21it/s]
838.445025707
350.324444097
-0.263258524342
808.752197363
309.852577662
-0.266624641812
 14%|█▍        | 179/1261 [00:28<02:55,  6.15it/s]
805.107087352
347.06793087
-0.281491852789
797.660127601
402.162405113
-0.301265123378
 14%|█▍        | 181/1261 [00:29<03:26,  5.24it/s]
718.176447996
395.12646699
-0.299751441516
710.245597027
408.118946364
-0.29726506423
 15%|█▍        | 184/1261 [00:29<02:54,  6.16it/s]
599.858411827
513.984619722
-0.321754564601
565.3462392
609.400814519
-0.332226178991
 15%|█▍        | 186/1261 [00:29<02:41,  6.67it/s]
516.808500506
474.644680141
-0.305219128623
459.899973894
407.097262051
-0.28240185827
 15%|█▍        | 187/1261 [00:29<02:36,  6.88it/s]
445.564847065
499.824523942
-0.30571085409
419.514160997
476.104162715
-0.310967792196
 15%|█▌        | 190/1261 [00:30<02:42,  6.57it/s]
426.412102859
472.275050899
-0.308345219864
425.61049428
488.84762312
-0.318790958554
 15%|█▌        | 192/1261 [00:30<02:37,  6.77it/s]
433.176618257
497.427160495
-0.334438021332
462.796241411
560.807884705
-0.349270899303
 15%|█▌        | 194/1261 [00:30<02:31,  7.05it/s]
476.079649093
534.178605756
-0.365789935616
508.406499769
624.74142693
-0.372174711353
 15%|█▌        | 195/1261 [00:31<02:29,  7.13it/s]
524.766434844
620.851056557
-0.373646936158
590.814538498
810.668481663
-0.3832636981
 16%|█▌        | 198/1261 [00:31<02:32,  6.97it/s]
628.179372724
923.522651482
-0.380901023668
690.172641888
1103.44531356
-0.383124157474
 16%|█▌        | 199/1261 [00:31<02:29,  7.11it/s]
719.929860045
1182.37554161
-0.376176278066
776.406029645
1017.84959029
-0.377025380781
 16%|█▌        | 202/1261 [00:32<02:33,  6.89it/s]
787.038506824
1051.23082575
-0.367216472953
760.159024534
988.80548916
-0.353224721489
 16%|█▌        | 204/1261 [00:32<02:34,  6.84it/s]
710.510792011
922.772886036
-0.340130843974
704.534224645
498.722547457
-0.320254628193
 16%|█▋        | 205/1261 [00:32<02:36,  6.77it/s]
700.585749009
614.21621094
-0.33392289092
683.655823544
685.521374198
-0.333714791457
 16%|█▋        | 207/1261 [00:32<02:38,  6.66it/s]
615.122876305
575.898255674
-0.334558183943
596.383890792
640.714150774
-0.335577218609
 17%|█▋        | 210/1261 [00:33<02:33,  6.83it/s]
616.264013613
731.619342159
-0.34671175808
639.263784162
666.037991856
-0.343576826668
 17%|█▋        | 211/1261 [00:33<02:30,  6.99it/s]
662.62540537
586.767805345
-0.326880159466
621.690774562
585.489014601
-0.326304299901
 17%|█▋        | 213/1261 [00:33<02:58,  5.87it/s]
590.088532284
561.554762179
-0.313237847359
550.053279515
776.041132404
-0.326366772492
 17%|█▋        | 215/1261 [00:34<03:01,  5.77it/s]
520.082877882
534.783641449
-0.302315847035
504.425486893
503.263412975
-0.29377493189
 17%|█▋        | 217/1261 [00:34<03:04,  5.65it/s]
520.056747942
746.953164797
-0.306695464674
 17%|█▋        | 218/1261 [00:34<03:10,  5.46it/s]
548.890122669
806.530297037
-0.306883787181
539.874686184
729.164560647
-0.303913352797
 17%|█▋        | 220/1261 [00:35<03:07,  5.56it/s]
585.813344516
1431.99941249
-0.338414079199
625.560883158
1849.86540455
-0.347513307624
 18%|█▊        | 222/1261 [00:35<03:00,  5.75it/s]
648.753053928
1170.32666568
-0.314524066592
683.047466438
1065.15528116
-0.31844286707
 18%|█▊        | 224/1261 [00:35<02:58,  5.82it/s]
657.59145595
960.440148985
-0.311517939311
696.510706208
1023.96231993
-0.311435180772
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[MoviePy] Done.
[MoviePy] >>>> Video ready: project_output_colour.mp4 

CPU times: user 9min 18s, sys: 8.3 s, total: 9min 26s
Wall time: 3min 20s

Discussion

  1. Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?

I had to spend some time to figure out the thresold for the gradient transformation and the src,dest functions for perspective transform. The test case worked only upon fine tuning these.

The pipeline doesnt perform very well in the challenge test case. I think the transform to keep the lane markings from previous stage for radius/offset under a certain threshold is not a very reliable mechanism. The pipeline needs to be fixed to be more accurate, I guess more time needs to be invested in thresholding and gradient part of the pipeline to fix this.

The pipeline wouldnt work well if the car deviates from the center of lane or does a lane change

In [ ]: